#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

import argparse
import logging
import math
import os
import random
import shutil
from pathlib import Path

import accelerate
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.state import AcceleratorState
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, upload_folder
from packaging import version
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
from transformers.utils import ContextManagers

import diffusers
from diffusers import (
    AutoencoderKL,
    DDPMScheduler,
    StableDiffusionPipeline,
    UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler

# from diffusers.training_utils import EMAModel
from src.diffusers_overwrite import EMAModel
from diffusers.utils import check_min_version, deprecate, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
from accelerate import DistributedDataParallelKwargs, DistributedType
import lpips


if is_wandb_available():
    import wandb


# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.18.0.dev0")

logger = get_logger(__name__, log_level="INFO")

DATASET_NAME_MAPPING = {
    "lambdalabs/pokemon-blip-captions": ("image", "text"),
}


def get_trainable_parameters(model):
    trainable_parameters = [p for p in model.parameters() if p.requires_grad == True]
    return trainable_parameters


def print_trainable_parameters(model):
    trainable_parameters = get_trainable_parameters(model)
    size = total_params = sum(p.numel() for p in trainable_parameters)
    units = ["B", "K", "M", "G"]
    unit_index = 0
    while size > 1024 and unit_index < len(units) - 1:
        size /= 1024
        unit_index += 1
    print(f"total trainable params : {size:.3f}{units[unit_index]}")


def get_t_schedule(t_schedule):
    if isinstance(t_schedule, str):
        ...
    else:
        # 处理自定义 t schedule , 返回一个分布，支持根据分布采样
        ...
    return t_schedule


def sample_timesteps(latents, t_schedule="uniform", num_train_timesteps=1000):
    """t_schedule 可以是 str 类型指定默认的分布 : uniform , t0 , t1 ; 或者是一个指定的分布类型"""
    bsz = latents.shape[0]
    if t_schedule == "uniform":  # Uniform(0,1)
        t = torch.rand((bsz,))
    elif t_schedule == "t0":
        t = torch.zeros((bsz,))
    elif t_schedule == "t1":
        t = torch.ones((bsz,))
    elif isinstance(t, ...):  # 自定义分布类型
        ...
    else:
        raise ValueError(f"t_schedule {t_schedule} is not supported")

    unscaled_timesteps = t.to(latents)
    timesteps = unscaled_timesteps * num_train_timesteps
    return unscaled_timesteps, timesteps


@torch.autocast("cuda")
def compute_loss(
    model_pred: torch.Tensor,
    target: torch.Tensor,
    loss_type="l2",
    pixel_loss=False,
    reduction="mean",
    loss_kwargs={},
):
    """loss type 支持 : l2 , l1 , lpips ; 如果需要使用多种 loss 混合，请传入字符串格式为 "loss_1,loss_2,loss_3,..."
    如果启动 pixel loss , 需要额外传入 vae decoder , noise , latents , 更改 model_pred 和 target
    可能出现 loss 过小导致不收敛的情况；所以 reduction 可选，目前支持  mean , sum
    """
    if pixel_loss:
        image_decoder = loss_kwargs["image_decoder"]
        noise = loss_kwargs["noise"]
        latents_pred = noise + model_pred
        target = loss_kwargs["pixel_values"]
        sclaed_latents_pred = 1 / image_decoder.config.scaling_factor * latents_pred
        model_pred = image_decoder.decode(sclaed_latents_pred).sample
        model_pred = model_pred.clamp(-1, 1)  # NOTE : this is essential

    if "lpips" in loss_type:
        assert pixel_loss, "lpips only support pixel loss"
        lpips_model = loss_kwargs["lpips_model"]

    loss = 0.
    if "l1" in loss_type:
        loss = loss + (model_pred - target).abs()
    if "l2" in loss_type:
        loss = loss + (model_pred - target).square()
    if "lpips" in loss_type:
        loss = loss + lpips_model(model_pred, target)
    
    assert not isinstance(loss, float) , "must compute loss to backpropogate "

    if reduction == "mean":
        loss = loss.mean()
    elif reduction == "sum":
        loss = loss.sum()

    return loss


def make_image_grid(imgs, rows, cols):
    assert len(imgs) == rows * cols

    w, h = imgs[0].size
    grid = Image.new("RGB", size=(cols * w, rows * h))

    for i, img in enumerate(imgs):
        grid.paste(img, box=(i % cols * w, i // cols * h))
    return grid


def save_model_card(
    args,
    repo_id: str,
    images=None,
    repo_folder=None,
):
    img_str = ""
    if len(images) > 0:
        image_grid = make_image_grid(images, 1, len(args.validation_prompts))
        image_grid.save(os.path.join(repo_folder, "val_imgs_grid.png"))
        img_str += "![val_imgs_grid](./val_imgs_grid.png)\n"

    yaml = f"""
---
license: creativeml-openrail-m
base_model: {args.pretrained_model_name_or_path}
datasets:
- {args.dataset_name}
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
    """
    model_card = f"""
# Text-to-image finetuning - {repo_id}

This pipeline was finetuned from **{args.pretrained_model_name_or_path}** on the **{args.dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: {args.validation_prompts}: \n
{img_str}

## Pipeline usage

You can use the pipeline like so:

```python
from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("{repo_id}", torch_dtype=torch.float16)
prompt = "{args.validation_prompts[0]}"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```

## Training info

These are the key hyperparameters used during training:

* Epochs: {args.num_train_epochs}
* Learning rate: {args.learning_rate}
* Batch size: {args.train_batch_size}
* Gradient accumulation steps: {args.gradient_accumulation_steps}
* Image resolution: {args.resolution}
* Mixed-precision: {args.mixed_precision}

"""
    wandb_info = ""
    if is_wandb_available():
        wandb_run_url = None
        if wandb.run is not None:
            wandb_run_url = wandb.run.url

    if wandb_run_url is not None:
        wandb_info = f"""
More information on all the CLI arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).
"""

    model_card += wandb_info

    with open(os.path.join(repo_folder, "README.md"), "w") as f:
        f.write(yaml + model_card)


def log_validation(
    vae, text_encoder, tokenizer, unet, args, accelerator, weight_dtype, epoch
):
    logger.info("Running validation... ")

    pipeline = StableDiffusionPipeline.from_pretrained(
        args.pretrained_model_name_or_path,
        vae=accelerator.unwrap_model(vae),
        text_encoder=accelerator.unwrap_model(text_encoder),
        tokenizer=tokenizer,
        unet=accelerator.unwrap_model(unet),
        safety_checker=None,
        revision=args.revision,
        torch_dtype=weight_dtype,
    )
    pipeline = pipeline.to(accelerator.device)
    pipeline.set_progress_bar_config(disable=True)

    if args.enable_xformers_memory_efficient_attention:
        pipeline.enable_xformers_memory_efficient_attention()

    if args.seed is None:
        generator = None
    else:
        generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)

    images = []
    for i in range(len(args.validation_prompts)):
        with torch.autocast("cuda"):
            image = pipeline(
                args.validation_prompts[i], num_inference_steps=20, generator=generator
            ).images[0]

        images.append(image)

    for tracker in accelerator.trackers:
        if tracker.name == "tensorboard":
            np_images = np.stack([np.asarray(img) for img in images])
            tracker.writer.add_images(
                "validation", np_images, epoch, dataformats="NHWC"
            )
        elif tracker.name == "wandb":
            tracker.log(
                {
                    "validation": [
                        wandb.Image(image, caption=f"{i}: {args.validation_prompts[i]}")
                        for i, image in enumerate(images)
                    ]
                }
            )
        else:
            logger.warn(f"image logging not implemented for {tracker.name}")

    del pipeline
    torch.cuda.empty_cache()

    return images


def parse_args():
    parser = argparse.ArgumentParser(description="Simple example of a training script.")
    parser.add_argument(
        "--input_perturbation",
        type=float,
        default=0,
        help="The scale of input perturbation. Recommended 0.1.",
    )
    parser.add_argument(
        "--pretrained_model_name_or_path",
        type=str,
        default=None,
        required=True,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
    )
    parser.add_argument(
        "--revision",
        type=str,
        default=None,
        required=False,
        help="Revision of pretrained model identifier from huggingface.co/models.",
    )
    parser.add_argument(
        "--dataset_name",
        type=str,
        default=None,
        help=(
            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
            " or to a folder containing files that 🤗 Datasets can understand."
        ),
    )
    parser.add_argument(
        "--dataset_config_name",
        type=str,
        default=None,
        help="The config of the Dataset, leave as None if there's only one config.",
    )
    parser.add_argument(
        "--train_data_dir",
        type=str,
        default=None,
        help=(
            "A folder containing the training data. Folder contents must follow the structure described in"
            " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
            " must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
        ),
    )
    parser.add_argument(
        "--image_column",
        type=str,
        default="image",
        help="The column of the dataset containing an image.",
    )
    parser.add_argument(
        "--caption_column",
        type=str,
        default="text",
        help="The column of the dataset containing a caption or a list of captions.",
    )
    parser.add_argument(
        "--max_train_samples",
        type=int,
        default=None,
        help=(
            "For debugging purposes or quicker training, truncate the number of training examples to this "
            "value if set."
        ),
    )
    parser.add_argument(
        "--validation_prompts",
        type=str,
        default=None,
        nargs="+",
        help=(
            "A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."
        ),
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="sd-model-finetuned",
        help="The output directory where the model predictions and checkpoints will be written.",
    )
    parser.add_argument(
        "--cache_dir",
        type=str,
        default=None,
        help="The directory where the downloaded models and datasets will be stored.",
    )
    parser.add_argument(
        "--seed", type=int, default=None, help="A seed for reproducible training."
    )
    parser.add_argument(
        "--resolution",
        type=int,
        default=512,
        help=(
            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
            " resolution"
        ),
    )
    parser.add_argument(
        "--center_crop",
        default=False,
        action="store_true",
        help=(
            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
            " cropped. The images will be resized to the resolution first before cropping."
        ),
    )
    parser.add_argument(
        "--random_flip",
        action="store_true",
        help="whether to randomly flip images horizontally",
    )
    parser.add_argument(
        "--train_batch_size",
        type=int,
        default=16,
        help="Batch size (per device) for the training dataloader.",
    )
    parser.add_argument("--num_train_epochs", type=int, default=100)
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=None,
        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--gradient_checkpointing",
        action="store_true",
        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=1e-4,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument(
        "--scale_lr",
        action="store_true",
        default=False,
        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
    )
    parser.add_argument(
        "--lr_scheduler",
        type=str,
        default="constant",
        help=(
            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
            ' "constant", "constant_with_warmup"]'
        ),
    )
    parser.add_argument(
        "--lr_warmup_steps",
        type=int,
        default=500,
        help="Number of steps for the warmup in the lr scheduler.",
    )
    parser.add_argument(
        "--snr_gamma",
        type=float,
        default=None,
        help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
        "More details here: https://arxiv.org/abs/2303.09556.",
    )
    parser.add_argument(
        "--use_8bit_adam",
        action="store_true",
        help="Whether or not to use 8-bit Adam from bitsandbytes.",
    )
    parser.add_argument(
        "--allow_tf32",
        action="store_true",
        help=(
            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
        ),
    )
    parser.add_argument(
        "--use_ema", action="store_true", help="Whether to use EMA model."
    )
    parser.add_argument(
        "--non_ema_revision",
        type=str,
        default=None,
        required=False,
        help=(
            "Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or"
            " remote repository specified with --pretrained_model_name_or_path."
        ),
    )
    parser.add_argument(
        "--dataloader_num_workers",
        type=int,
        default=0,
        help=(
            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
        ),
    )
    parser.add_argument(
        "--adam_beta1",
        type=float,
        default=0.9,
        help="The beta1 parameter for the Adam optimizer.",
    )
    parser.add_argument(
        "--adam_beta2",
        type=float,
        default=0.999,
        help="The beta2 parameter for the Adam optimizer.",
    )
    parser.add_argument(
        "--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use."
    )
    parser.add_argument(
        "--adam_epsilon",
        type=float,
        default=1e-08,
        help="Epsilon value for the Adam optimizer",
    )
    parser.add_argument(
        "--max_grad_norm", default=1.0, type=float, help="Max gradient norm."
    )
    parser.add_argument(
        "--push_to_hub",
        action="store_true",
        help="Whether or not to push the model to the Hub.",
    )
    parser.add_argument(
        "--hub_token",
        type=str,
        default=None,
        help="The token to use to push to the Model Hub.",
    )
    parser.add_argument(
        "--prediction_type",
        type=str,
        default=None,
        help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.",
    )
    parser.add_argument(
        "--hub_model_id",
        type=str,
        default=None,
        help="The name of the repository to keep in sync with the local `output_dir`.",
    )
    parser.add_argument(
        "--logging_dir",
        type=str,
        default="logs",
        help=(
            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
        ),
    )
    parser.add_argument(
        "--mixed_precision",
        type=str,
        default=None,
        choices=["no", "fp16", "bf16"],
        help=(
            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
        ),
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="tensorboard",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
        ),
    )
    parser.add_argument(
        "--local_rank",
        type=int,
        default=-1,
        help="For distributed training: local_rank",
    )
    parser.add_argument(
        "--checkpointing_steps",
        type=int,
        default=500,
        help=(
            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
            " training using `--resume_from_checkpoint`."
        ),
    )
    parser.add_argument(
        "--checkpoints_total_limit",
        type=int,
        default=None,
        help=("Max number of checkpoints to store."),
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help=(
            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
        ),
    )
    parser.add_argument(
        "--enable_xformers_memory_efficient_attention",
        action="store_true",
        help="Whether or not to use xformers.",
    )
    parser.add_argument(
        "--noise_offset", type=float, default=0, help="The scale of noise offset."
    )
    parser.add_argument(
        "--validation_epochs",
        type=int,
        default=5,
        help="Run validation every X epochs.",
    )
    parser.add_argument(
        "--tracker_project_name",
        type=str,
        default="text2image-fine-tune",
        help=(
            "The `project_name` argument passed to Accelerator.init_trackers for"
            " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
        ),
    )

    parser.add_argument(
        "--unet_from_config",
        type=str,
        default=None,
    )
    parser.add_argument(
        "--load_pretrained_weights",
        type=str,
        default=None,
    )

    parser.add_argument(
        "--overwrite_dataset",
        action="store_true",
        default=False,
    )

    parser.add_argument(
        "--pixel_loss",
        action="store_true",
        default=False,
    )
    parser.add_argument(
        "--loss_type",
        type=str,
        default="l2",
    )
    parser.add_argument(
        "--t_schedule",
        type=str,
        default="uniform",
        choices=["uniform", "t0", "t1", "custom"],
    )
    parser.add_argument(
        "--reduction_method",
        type=str,
        default="mean",
        choices=["mean", "sum"],
    )
    parser.add_argument(
        "--p_uncond",
        type=float,
        default=0.1,
    )
    parser.add_argument(
        "--vae_name_or_path",
        type=str,
        default=None,
    )

    args = parser.parse_args()
    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
    if env_local_rank != -1 and env_local_rank != args.local_rank:
        args.local_rank = env_local_rank

    # Sanity checks
    if args.dataset_name is None and args.train_data_dir is None:
        raise ValueError("Need either a dataset name or a training folder.")

    # default to using the same revision for the non-ema model if not specified
    if args.non_ema_revision is None:
        args.non_ema_revision = args.revision

    if args.pixel_loss:
        assert args.vae_name_or_path is not None, "must use better vae for pixel loss"

    return args


def main():
    args = parse_args()

    if args.non_ema_revision is not None:
        deprecate(
            "non_ema_revision!=None",
            "0.15.0",
            message=(
                "Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to"
                " use `--variant=non_ema` instead."
            ),
        )
    logging_dir = os.path.join(args.output_dir, args.logging_dir)

    accelerator_project_config = ProjectConfiguration(
        project_dir=args.output_dir, logging_dir=logging_dir
    )

    ddp_kwargs = DistributedDataParallelKwargs()
    accelerator = Accelerator(
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        mixed_precision=args.mixed_precision,
        log_with=args.report_to,
        project_config=accelerator_project_config,
        kwargs_handlers=[ddp_kwargs],
    )

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_warning()
        diffusers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
        diffusers.utils.logging.set_verbosity_error()

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)

        if args.push_to_hub:
            repo_id = create_repo(
                repo_id=args.hub_model_id or Path(args.output_dir).name,
                exist_ok=True,
                token=args.hub_token,
            ).repo_id

    # Load scheduler, tokenizer and models.
    # noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
    tokenizer = CLIPTokenizer.from_pretrained(
        args.pretrained_model_name_or_path,
        subfolder="tokenizer",
        revision=args.revision,
    )

    def deepspeed_zero_init_disabled_context_manager():
        """
        returns either a context list that includes one that will disable zero.Init or an empty context list
        """
        deepspeed_plugin = (
            AcceleratorState().deepspeed_plugin
            if accelerate.state.is_initialized()
            else None
        )
        if deepspeed_plugin is None:
            return []

        return [deepspeed_plugin.zero3_init_context_manager(enable=False)]

    # Currently Accelerate doesn't know how to handle multiple models under Deepspeed ZeRO stage 3.
    # For this to work properly all models must be run through `accelerate.prepare`. But accelerate
    # will try to assign the same optimizer with the same weights to all models during
    # `deepspeed.initialize`, which of course doesn't work.
    #
    # For now the following workaround will partially support Deepspeed ZeRO-3, by excluding the 2
    # frozen models from being partitioned during `zero.Init` which gets called during
    # `from_pretrained` So CLIPTextModel and AutoencoderKL will not enjoy the parameter sharding
    # across multiple gpus and only UNet2DConditionModel will get ZeRO sharded.
    with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
        text_encoder = CLIPTextModel.from_pretrained(
            args.pretrained_model_name_or_path,
            subfolder="text_encoder",
            revision=args.revision,
        )
        if args.vae_name_or_path is not None:
            vae_name_or_path = args.vae_name_or_path
        else:
            vae_name_or_path = args.pretrained_model_name_or_path
        vae = AutoencoderKL.from_pretrained(
            vae_name_or_path, subfolder="vae", revision=args.revision
        )

    # NOTE : can create new model here
    if args.unet_from_config is not None:
        unet_config = UNet2DConditionModel.load_config(args.unet_from_config)
        unet = UNet2DConditionModel.from_config(unet_config)
    elif args.load_pretrained_weights is not None:
        unet = UNet2DConditionModel.from_pretrained(
            args.load_pretrained_weights, revision=args.non_ema_revision
        )
    else:
        unet = UNet2DConditionModel.from_pretrained(
            args.pretrained_model_name_or_path,
            subfolder="unet",
            revision=args.non_ema_revision,
        )

    # Freeze vae and text_encoder
    vae.requires_grad_(False)
    text_encoder.requires_grad_(False)

    model = unet
    print_trainable_parameters(model)

    # Create EMA for the unet.
    if args.use_ema:
        # ema_unet = UNet2DConditionModel.from_pretrained(
        #     args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
        # )
        # ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)
        ema_model = EMAModel(get_trainable_parameters(model))
        accelerator.register_for_checkpointing(ema_model)

    if args.enable_xformers_memory_efficient_attention:
        if is_xformers_available():
            import xformers

            xformers_version = version.parse(xformers.__version__)
            if xformers_version == version.parse("0.0.16"):
                logger.warn(
                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
                )
            model.enable_xformers_memory_efficient_attention()
        else:
            raise ValueError(
                "xformers is not available. Make sure it is installed correctly"
            )

    def compute_snr(timesteps):
        """
        Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
        """
        alphas_cumprod = noise_scheduler.alphas_cumprod
        sqrt_alphas_cumprod = alphas_cumprod**0.5
        sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5

        # Expand the tensors.
        # Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
        sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[
            timesteps
        ].float()
        while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
            sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
        alpha = sqrt_alphas_cumprod.expand(timesteps.shape)

        sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(
            device=timesteps.device
        )[timesteps].float()
        while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
            sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
        sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)

        # Compute SNR.
        snr = (alpha / sigma) ** 2
        return snr

    # NOTE : no need to do this
    # `accelerate` 0.16.0 will have better support for customized saving
    if version.parse(accelerate.__version__) >= version.parse("0.16.0") and False:
        # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
        def save_model_hook(models, weights, output_dir):
            if args.use_ema:
                ema_unet.save_pretrained(os.path.join(output_dir, "unet_ema"))

            for i, model in enumerate(models):
                model.save_pretrained(os.path.join(output_dir, "unet"))

                # make sure to pop weight so that corresponding model is not saved again
                weights.pop()

        def load_model_hook(models, input_dir):
            if args.use_ema:
                load_model = EMAModel.from_pretrained(
                    os.path.join(input_dir, "unet_ema"), UNet2DConditionModel
                )
                ema_unet.load_state_dict(load_model.state_dict())
                ema_unet.to(accelerator.device)
                del load_model

            for i in range(len(models)):
                # pop models so that they are not loaded again
                model = models.pop()

                # load diffusers style into model
                load_model = UNet2DConditionModel.from_pretrained(
                    input_dir, subfolder="unet"
                )
                model.register_to_config(**load_model.config)

                model.load_state_dict(load_model.state_dict())
                del load_model

        accelerator.register_save_state_pre_hook(save_model_hook)
        accelerator.register_load_state_pre_hook(load_model_hook)

    if args.gradient_checkpointing:
        model.enable_gradient_checkpointing()

    # Enable TF32 for faster training on Ampere GPUs,
    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
    if args.allow_tf32:
        torch.backends.cuda.matmul.allow_tf32 = True

    if args.scale_lr:
        args.learning_rate = (
            args.learning_rate
            * args.gradient_accumulation_steps
            * args.train_batch_size
            * accelerator.num_processes
        )

    # Initialize the optimizer
    if args.use_8bit_adam:
        try:
            import bitsandbytes as bnb
        except ImportError:
            raise ImportError(
                "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
            )

        optimizer_cls = bnb.optim.AdamW8bit
    else:
        optimizer_cls = torch.optim.AdamW

    trainable_params_list = get_trainable_parameters(model)
    trainable_params = trainable_params_list
    optimizer = optimizer_cls(
        trainable_params,
        lr=args.learning_rate,
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )

    # Get the datasets: you can either provide your own training and evaluation files (see below)
    # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).

    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
    # download the dataset.
    if not args.overwrite_dataset:
        if args.dataset_name is not None:
            # Downloading and loading a dataset from the hub.
            dataset = load_dataset(
                args.dataset_name,
                args.dataset_config_name,
                cache_dir=args.cache_dir,
            )
        else:
            data_files = {}
            if args.train_data_dir is not None:
                data_files["train"] = os.path.join(args.train_data_dir, "**")
            dataset = load_dataset(
                "imagefolder",
                data_files=data_files,
                cache_dir=args.cache_dir,
            )
            # See more about loading custom images at
            # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder

        # Preprocessing the datasets.
        # We need to tokenize inputs and targets.
        column_names = dataset["train"].column_names

        # 6. Get the column names for input/target.
        dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
        if args.image_column is None:
            image_column = (
                dataset_columns[0] if dataset_columns is not None else column_names[0]
            )
        else:
            image_column = args.image_column
            if image_column not in column_names:
                raise ValueError(
                    f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
                )
        if args.caption_column is None:
            caption_column = (
                dataset_columns[1] if dataset_columns is not None else column_names[1]
            )
        else:
            caption_column = args.caption_column
            if caption_column not in column_names:
                raise ValueError(
                    f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
                )

        # Preprocessing the datasets.
        # We need to tokenize input captions and transform the images.
        def tokenize_captions(examples, is_train=True):
            captions = []
            for caption in examples[caption_column]:
                if isinstance(caption, str):
                    captions.append(caption)
                elif isinstance(caption, (list, np.ndarray)):
                    # take a random caption if there are multiple
                    captions.append(random.choice(caption) if is_train else caption[0])
                else:
                    raise ValueError(
                        f"Caption column `{caption_column}` should contain either strings or lists of strings."
                    )
            inputs = tokenizer(
                captions,
                max_length=tokenizer.model_max_length,
                padding="max_length",
                truncation=True,
                return_tensors="pt",
            )
            # return inputs.input_ids
            return inputs

        # Preprocessing the datasets.
        train_transforms = transforms.Compose(
            [
                transforms.Resize(
                    args.resolution, interpolation=transforms.InterpolationMode.BILINEAR
                ),
                transforms.CenterCrop(args.resolution)
                if args.center_crop
                else transforms.RandomCrop(args.resolution),
                transforms.RandomHorizontalFlip()
                if args.random_flip
                else transforms.Lambda(lambda x: x),
                transforms.ToTensor(),
                transforms.Normalize([0.5], [0.5]),
            ]
        )

        import io

        def preprocess_train(examples):
            # NOTE:  因为网络问题把数据集下载到本地，结果发现是用 bytes 格式存储的，需要先转换成 pil image
            # images = [image.convert("RGB") for image in examples[image_column]]
            image_column_data = examples[image_column]
            images = [
                Image.open(io.BytesIO(d["bytes"])).convert("RGB")
                for d in image_column_data
            ]

            examples["pixel_values"] = [train_transforms(image) for image in images]
            examples["input_ids"] = tokenize_captions(examples)
            return examples

        with accelerator.main_process_first():
            if args.max_train_samples is not None:
                dataset["train"] = (
                    dataset["train"]
                    .shuffle(seed=args.seed)
                    .select(range(args.max_train_samples))
                )
            # Set the training transforms
            train_dataset = dataset["train"].with_transform(preprocess_train)

    else:
        # NOTE : 自定义要训练的 dataset
        from reflow.data.dataset import CocoCaption

        image_transforms = transforms.Compose(
            [
                transforms.Resize(
                    args.resolution,
                ),
                transforms.CenterCrop(args.resolution)
                if args.center_crop
                else transforms.RandomCrop(args.resolution),
                transforms.RandomHorizontalFlip()
                if args.random_flip
                else transforms.Lambda(lambda x: x),
                transforms.ToTensor(),
                transforms.Normalize([0.5], [0.5]),
            ]
        )
        assert args.dataset_name == "coco2014"
        train_dataset = CocoCaption(
            data_root="data/coco2014",
            version="2014",
            phase="train",
            image_transforms=image_transforms,
            tokenizer=tokenizer,
            use_hf_key_format=True,
            p_uncond=args.p_uncond,
        )

    def collate_fn(examples):
        pixel_values = torch.stack([example["pixel_values"] for example in examples])
        pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
        input_ids = torch.stack([example["input_ids"] for example in examples])
        attention_mask = torch.stack(
            [example["attention_mask"] for example in examples]
        )
        return {
            "pixel_values": pixel_values,
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }

    # DataLoaders creation:
    train_dataloader = torch.utils.data.DataLoader(
        train_dataset,
        shuffle=True,
        collate_fn=collate_fn,
        batch_size=args.train_batch_size,
        num_workers=args.dataloader_num_workers,
    )

    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    num_update_steps_per_epoch = math.ceil(
        len(train_dataloader) / args.gradient_accumulation_steps
    )
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True

    lr_scheduler = get_scheduler(
        args.lr_scheduler,
        optimizer=optimizer,
        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
    )

    # Prepare everything with our `accelerator`.
    model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        model, optimizer, train_dataloader, lr_scheduler
    )

    # if args.use_ema:
    #     ema_unet.to(accelerator.device)

    # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision
    # as these weights are only used for inference, keeping weights in full precision is not required.
    weight_dtype = torch.float32
    if accelerator.mixed_precision == "fp16":
        weight_dtype = torch.float16
        args.mixed_precision = accelerator.mixed_precision
    elif accelerator.mixed_precision == "bf16":
        weight_dtype = torch.bfloat16
        args.mixed_precision = accelerator.mixed_precision

    # Move text_encode and vae to gpu and cast to weight_dtype
    text_encoder.to(accelerator.device, dtype=weight_dtype)
    vae.to(accelerator.device, dtype=weight_dtype)

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = math.ceil(
        len(train_dataloader) / args.gradient_accumulation_steps
    )
    if overrode_max_train_steps:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    # We need to initialize the trackers we use, and also store our configuration.
    # The trackers initializes automatically on the main process.
    if accelerator.is_main_process:
        tracker_config = dict(vars(args))
        tracker_config.pop("validation_prompts")
        accelerator.init_trackers(args.tracker_project_name, tracker_config)

    # Train!
    total_batch_size = (
        args.train_batch_size
        * accelerator.num_processes
        * args.gradient_accumulation_steps
    )

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num Epochs = {args.num_train_epochs}")
    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
    logger.info(
        f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
    )
    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
    logger.info(f"  Total optimization steps = {args.max_train_steps}")
    global_step = 0
    first_epoch = 0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint != "latest":
            path = os.path.basename(args.resume_from_checkpoint)
        else:
            # Get the most recent checkpoint
            dirs = os.listdir(args.output_dir)
            dirs = [d for d in dirs if d.startswith("checkpoint")]
            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
            path = dirs[-1] if len(dirs) > 0 else None

        if path is None:
            accelerator.print(
                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
            )
            args.resume_from_checkpoint = None
        else:
            accelerator.print(f"Resuming from checkpoint {path}")
            accelerator.load_state(os.path.join(args.output_dir, path))
            global_step = int(path.split("-")[1])

            resume_global_step = global_step * args.gradient_accumulation_steps
            first_epoch = global_step // num_update_steps_per_epoch
            resume_step = resume_global_step % (
                num_update_steps_per_epoch * args.gradient_accumulation_steps
            )
    start_step = first_epoch * num_update_steps_per_epoch

    # NOTE : 默认会把 custom state 加载到 cpu 上
    if args.use_ema:
        ema_model.to(accelerator.device)
    if "lpips" in args.loss_type:
        lpips_model = lpips.LPIPS(net="vgg")
        lpips_model.to(accelerator.device, dtype=weight_dtype).requires_grad_(False)
    # NOTE : no need to do this
    if args.pixel_loss and False:
        vae.to(dtype=torch.float32)
        lpips_model.to(dtype=torch.float32)

    # Only show the progress bar once on each machine.
    progress_bar = tqdm(
        range(start_step, args.max_train_steps),
        disable=not accelerator.is_local_main_process,
    )
    progress_bar.set_description("Steps")

    for epoch in range(first_epoch, args.num_train_epochs):
        model.train()
        train_loss = 0.0
        for step, batch in enumerate(train_dataloader):
            # Skip steps until we reach the resumed step
            if (
                args.resume_from_checkpoint
                and epoch == first_epoch
                and step < resume_step
            ):
                if step % args.gradient_accumulation_steps == 0:
                    progress_bar.update(1)
                continue

            # with accelerator.accumulate(unet):
            with accelerator.accumulate(model):
                with accelerator.autocast():
                    # Convert images to latent space
                    latents = vae.encode(
                        batch["pixel_values"].to(weight_dtype)
                    ).latent_dist.sample()
                    latents = latents * vae.config.scaling_factor

                    # TODO : 定义并支持自定义分布和采样
                    noise = torch.randn_like(latents)
                    unscaled_timesteps, timesteps = sample_timesteps(
                        latents, t_schedule=get_t_schedule(args.t_schedule)
                    )
                    # NOTE : timesteps 并不是查表类型，所以不用转成整数
                    # NOTE : unscaled_timesteps 是 (0,1) timesteps 是为了兼容 Unet2d 的实现，乘以 num_train_timesteps 作为系数

                    # Get the text embedding for conditioning
                    # TODO : 这里以及后面的 unet 部分并不一定要使用 mask ; 如果是从预训练模型初始化，使用方式要保持和预训练模型对齐
                    encoder_hidden_states = text_encoder(
                        batch["input_ids"], attention_mask=batch["attention_mask"]
                    )[0]
                    # 线性插值得到 noise 和 latent 连线上的中间点
                    t_weight = unscaled_timesteps.view(-1, 1, 1, 1)
                    noisy_latents = t_weight * latents + (1.0 - t_weight) * noise
                    target = latents - noise

                    # Predict the noise residual and compute loss
                    model_pred = model(
                        noisy_latents, timesteps, encoder_hidden_states
                    ).sample
                    # TODO : 可以确定，还没有支持 attention_mask 和 xformers 同时使用
                    # model_pred = model(
                    #     noisy_latents,
                    #     timesteps,
                    #     encoder_hidden_states,
                    #     encoder_attention_mask=batch["attention_mask"],
                    # ).sample

                    loss_kwargs = {}
                    if args.pixel_loss:
                        loss_kwargs["noise"] = noise
                        loss_kwargs["pixel_values"] = batch["pixel_values"]
                        loss_kwargs["image_decoder"] = vae
                    if "lpips" in args.loss_type:
                        loss_kwargs["lpips_model"] = lpips_model
                    loss = compute_loss(
                        model_pred,
                        target,
                        args.loss_type,
                        args.pixel_loss,
                        args.reduction_method,
                        loss_kwargs,
                    )

                # Gather the losses across all processes for logging (if we use distributed training).
                avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
                train_loss += avg_loss.item() / args.gradient_accumulation_steps

                # Backpropagate
                accelerator.backward(loss)
                if accelerator.sync_gradients:
                    accelerator.clip_grad_norm_(
                        get_trainable_parameters(model), args.max_grad_norm
                    )
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                if args.use_ema:
                    ema_model.step(get_trainable_parameters(model))
                progress_bar.update(1)
                global_step += 1
                accelerator.log({"train_loss": train_loss}, step=global_step)
                train_loss = 0.0

                if global_step % args.checkpointing_steps == 0:
                    # 让主进程执行一些清理工作
                    if accelerator.is_main_process:
                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
                        if args.checkpoints_total_limit is not None:
                            checkpoints = os.listdir(args.output_dir)
                            checkpoints = [
                                d for d in checkpoints if d.startswith("checkpoint")
                            ]
                            checkpoints = sorted(
                                checkpoints, key=lambda x: int(x.split("-")[1])
                            )

                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
                            if len(checkpoints) >= args.checkpoints_total_limit:
                                num_to_remove = (
                                    len(checkpoints) - args.checkpoints_total_limit + 1
                                )
                                removing_checkpoints = checkpoints[0:num_to_remove]

                                logger.info(
                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
                                )
                                logger.info(
                                    f"removing checkpoints: {', '.join(removing_checkpoints)}"
                                )

                                for removing_checkpoint in removing_checkpoints:
                                    removing_checkpoint = os.path.join(
                                        args.output_dir, removing_checkpoint
                                    )
                                    shutil.rmtree(removing_checkpoint)

                    if (
                        accelerator.distributed_type != DistributedType.DEEPSPEED
                        and accelerator.is_main_process
                    ):
                        save_path = os.path.join(
                            args.output_dir, f"checkpoint-{global_step}"
                        )
                        accelerator.save_state(save_path)
                        logger.info(f"Saved state to {save_path}")
                    elif accelerator.distributed_type == DistributedType.DEEPSPEED:
                        # NOTE : deepspeed 需要在每个进程调用保存函数；check it
                        save_path = os.path.join(
                            args.output_dir, f"checkpoint-{global_step}"
                        )
                        accelerator.save_state(save_path)

                    # NOTE : 保存所有 learnable weights
                    if accelerator.is_main_process:
                        unwrapped_model = accelerator.unwrap_model(model)
                        if args.use_ema:
                            ema_model.copy_to(get_trainable_parameters(unwrapped_model))

                        unet_to_save = unwrapped_model
                        unet_save_dir = os.path.join(
                            args.output_dir, f"weights-{global_step}", "unet"
                        )
                        os.makedirs(unet_save_dir, exist_ok=True)
                        unet_to_save.save_pretrained(unet_save_dir)

            logs = {
                "step_loss": loss.detach().item(),
                "lr": lr_scheduler.get_last_lr()[0],
            }
            progress_bar.set_postfix(**logs)

            if global_step >= args.max_train_steps:
                break

        if accelerator.is_main_process and False:
            if (
                args.validation_prompts is not None
                and epoch % args.validation_epochs == 0
            ):
                if args.use_ema:
                    # Store the UNet parameters temporarily and load the EMA parameters to perform inference.
                    ema_unet.store(unet.parameters())
                    ema_unet.copy_to(unet.parameters())
                log_validation(
                    vae,
                    text_encoder,
                    tokenizer,
                    unet,
                    args,
                    accelerator,
                    weight_dtype,
                    global_step,
                )
                if args.use_ema:
                    # Switch back to the original UNet parameters.
                    ema_unet.restore(unet.parameters())

    # Create the pipeline using the trained modules and save it.
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        unwrapped_model = accelerator.unwrap_model(model)
        if args.use_ema:
            ema_model.copy_to(get_trainable_parameters(unwrapped_model))

        unet_to_save = unwrapped_model
        unet_save_dir = os.path.join(args.output_dir, f"weights-{global_step}", "unet")
        os.makedirs(unet_save_dir, exist_ok=True)
        unet_to_save.save_pretrained(unet_save_dir)

        # Run a final round of inference.
        images = []
        if args.validation_prompts is not None and False:
            logger.info("Running inference for collecting generated images...")
            pipeline = pipeline.to(accelerator.device)
            pipeline.torch_dtype = weight_dtype
            pipeline.set_progress_bar_config(disable=True)

            if args.enable_xformers_memory_efficient_attention:
                pipeline.enable_xformers_memory_efficient_attention()

            if args.seed is None:
                generator = None
            else:
                generator = torch.Generator(device=accelerator.device).manual_seed(
                    args.seed
                )

            for i in range(len(args.validation_prompts)):
                with torch.autocast("cuda"):
                    image = pipeline(
                        args.validation_prompts[i],
                        num_inference_steps=20,
                        generator=generator,
                    ).images[0]
                images.append(image)

        if args.push_to_hub and False:
            save_model_card(args, repo_id, images, repo_folder=args.output_dir)
            upload_folder(
                repo_id=repo_id,
                folder_path=args.output_dir,
                commit_message="End of training",
                ignore_patterns=["step_*", "epoch_*"],
            )

    accelerator.end_training()


if __name__ == "__main__":
    main()
